332 results about "Context model" patented technology

An artificial intelligence (Al) assisted generative digital task fulfillment process within digital model platforms is provided. Disclosed are methods and systems for carrying out digital tasks through generative Al, including tasks related to the streamlined design, validation, verification, certification, assembly, operations, and maintenance processes of complex systems. Hie method includes receiving access to a context Al model trained on Internet-scale data, receiving a user prompt indicating the digital task, and generating contextual data based on the user prompt using the context Al model, where the contextual data identifies a syntax Al model. The method includes training the syntax Al model to generate a template script having a placeholder variable for a parameter related to the digital task. The method also includes using a parameter substitution process to generate the orchestration script by substituting the variable with a parameter value.

The invention discloses a focused mode context retrieval enhancement method and system in a knowledge retrieval system, and the method comprises the steps: carrying out the format conversion and preprocessing of a knowledge document, segmenting a text through a focused overlapping partitioning algorithm, and extracting structural features through a natural language processing technology to generate a directory; the text blocks are converted into semantic vectors through a BERT-Context model, and the semantic vectors are stored in a vector library; during retrieval, text blocks are quickly positioned according to question similarity, a long context is formed through splicing, similarity ranking is calculated, cue words are generated by high-correlation text blocks and questions together, and answers are obtained by inputting the cue words into a generation model. According to the method, the problems of semantic deficiency, incomplete context information and undetailed user retrieval context in the existing knowledge retrieval system can be effectively solved, the accuracy and integrity of knowledge retrieval are improved, and particularly, the method is excellent in performance when processing problems in complex fields.

The invention relates to the field of intelligent interaction, and discloses an intelligent hardware dynamic interaction system based on voice semantic fusion and multi-modal perception, which comprises the following steps of: constructing a context model of continuous operation by collecting continuous voice instructions, gesture actions and expression information of a user; semantic analysis and feature fusion are carried out on currently collected voice, gesture and expression features, meanwhile, credibility indexes of all modes are calculated through a weighting or deep learning model, weighting correction is carried out on a fusion result, a real-time feedback algorithm is adopted for weight adjustment for continuous optimization, the next operation intention of a user is predicted through deep learning, and the user experience is improved. And in combination with historical interaction data, online feedback and prediction errors, context management, modal weight and intention prediction strategies are adaptively optimized, and the updated strategies are used for next-round context acquisition and multi-modal fusion. The method has the advantage of improving the recognition accuracy in the continuous interaction scene.

Systems, apparatuses, and methods are provided herein for the adaptive management of a network of devices. The system is configured to train a context model, receive signals from the network of devices, determine, based on the context model, context data associate with a current condition of the network of devices, determine a formation plan based on the context data, configure one or more scout applications based on the formation plan and device information stored in the network device database, and cause the one or more scout applications to be executed by the one or more devices in the network of devices.

The invention relates to the technical field of image compression, in particular to a hierarchical multi-context space adaptive image compression method. The method comprises the steps of obtaining a training set and a test set; constructing a spatial adaptive feature modulation network, wherein the spatial adaptive feature modulation network comprises an encoder, a decoder, a super-prior encoder and a super-prior decoder; constructing a multi-context joint entropy estimation model, wherein the multi-context joint entropy estimation model comprises a local context model, a channel context model, an in-layer anchor point context model and a cross-layer global context model; obtaining an image compression network in combination with the spatial adaptive feature modulation network and the multi-context joint entropy estimation model; respectively training and testing an image compression network by using the training set and the test set to obtain a trained image compression network; and inputting a to-be-processed image into the trained image compression network to obtain a reconstructed image. According to the invention, the image compression effect is improved.

Disclosed systems and methods obtain a graph of a polymer comprising nodes and edges, the nodes representing polymer atoms, the edges encoding distances and relative orientations between corresponding pairs of nodes and whether pairs of nodes are covalently bound. Each subgraph in a plurality of first partial-context subgraphs of the graph is sequentially inputted into a first model to calculate first side chain dihedral angles for polymer residues. This updates the graph through first side chain dihedral angles. Each second subgraph in a plurality of second partial-context subgraphs of the updated graph is inputted into a second model, thereby obtaining calculated second side chain dihedral angles for polymer residues that serve to update the graph through second side chain dihedral angles. The graph is again updated with updated side chain dihedral angle values obtained by sequentially inputting full-context subgraphs, each such subgraph representing a different residue, into a full-context model.

The invention discloses an end-to-end image lossless compression method based on two-dimensional inseparable wavelet transform. According to the method, the image is decomposed through multistage wavelet transform, and sub-band coefficients of different sizes are obtained. And inputting the coefficients into an entropy coding model, and using a context model to drive an arithmetic encoder to compress the coefficients into a bit stream. According to the method, two-dimensional inseparable wavelet transformation is adopted, a lifting structure of the wavelet transformation is used, three prediction filters and an updating filter are included, and the filter structure is a convolutional neural network structure. The entropy coding module also uses a neural network structure. Carrying out the overall optimization of all parameters through deep learning, training the network until convergence, and achieving the lossless compression of an image through a trained model. The end-to-end image lossless compression method shows excellent performance in the aspect of lossless compression, is remarkably improved compared with a traditional JPEG2000 lossless compression method, and is also superior to an end-to-end image lossless compression method based on traditional separable wavelet transform.

A video decoding method includes obtaining a to-be-entropy-decoded syntax element in a current block by parsing a received bitstream, where the to-be-entropy-decoded syntax element includes a syntax element 1 or a syntax element 2 in the current block, obtaining a context model corresponding to the to-be-entropy-decoded syntax element, where both of a context model corresponding to the syntax element 1 and a context model corresponding to the syntax element 2 are determined from the same preset context model set, entropy decoding the to-be-entropy-decoded syntax element based on the context model corresponding to the to-be-entropy-decoded syntax element, and obtaining a reconstructed image of the current block based on the syntax element obtained by entropy decoding.

The invention relates to a large language model dynamic routing method and device based on context learning model characterization and a readable storage medium. Query is embedded and mapped to a language model input space by using a projection model, semantic alignment is realized, a representative evaluation set covering multi-dimensional capability is automatically screened from a benchmark question bank, and the representative evaluation set is used for evaluating the multi-dimensional capability. Performance characteristics of the model on an evaluation set are efficiently obtained at a time, and high-quality context model capability representation is formed; then real-time query embedding and context model capability representation are combined, a lightweight routing language model is used for supervised learning, so that the fine-grained model distinguishing capability is achieved, an increment embedding updating mechanism is designed, and when a new model is accessed or an old model is upgraded, cold start can be rapidly completed only through a very small number of fixed questions, so that the efficiency is improved. The calculation and maintenance cost is greatly reduced, and the accuracy, real-time performance and flexible expansibility of model routing are effectively improved.

A data platform monitors a compute environment by performing multi-stage heuristic analysis of event data representing a plurality of events occurring within the environment. The platform utilizes multiple event analyzers, each configured according to a distinct analysis heuristic, to evaluate different subsets of the event data and generate corresponding output signals. A higher-level event analyzer applies a further heuristic to the multiple output signals to generate a composite alert signal, indicating whether the combination of analyzed events collectively represents a security intrusion or other anomalous condition of sufficient severity to warrant alerting. Based on the composite alert signal, the platform performs an alert-based operation, such as generating a user-facing alert, initiating an automated mitigation, or updating a contextual model of system behavior. By combining the analytical outputs of heterogeneous heuristics, the disclosed architecture enhances the accuracy and contextual relevance of automated intrusion detection within complex computing environments.

The invention discloses an enterprise data analysis system based on natural language interaction, and relates to the field of enterprise data analysis, the system comprises seven core function modules: a dialogue management module supports a user to create a new dialogue and automatically jumps to an initial page; the report interaction module realizes query support and switching functions of a real-time report and a T + 1 report through a floating button; the file import module allows uploading of Word, PDF and JPG format files to supplement question and answer contexts; the model configuration module supports dynamic switching of different AI service interfaces; the interaction auxiliary module provides a quick question button and a dialogue interruption function; the report preview module integrates Excel and PDF plug-ins to realize detail data preview; the session sharing module generates a sharing link and sets an access permission rule; dynamic model configuration optimizes response speed and accuracy; the operation process is simplified by a suspension button and a quick questioning function; the session sharing mechanism supports cross-team cooperation; and the real-time collaborative design with the T + 1 report meets the multi-dimensional data analysis requirement.

The invention relates to probability estimation in multi-symbol entropy coding. Entropy coding, such as multi-symbol arithmetic coding, is used for video compression to encode data into a compressed bitstream for transmission. Some entropy coding techniques are adaptive, which means that the probability distribution is updated instantly based on data. Accuracy of cumulative probability estimation in adaptive multi-symbol arithmetic coding may affect coding efficiency. To solve this problem, a mixture of two or more adaptive cumulative probability estimates calculated using two or more adaptation parameters may be used in place of a single cumulative probability estimate. The two or more adaptation parameters may be unique to the context model. The divergence in the adaptive cumulative probability estimation may represent a sudden change in the probability of the symbol. The divergence may trigger a reset of one or more adaptive cumulative probability estimates.

Methods for video decoding and encoding, apparatuses and non-transitory computer-readable storage media thereof are provided. In one method for video decoding, a binary arithmetic decoder may obtain a first probability for a binary symbol according to a first adaptation parameter, where the binary symbol is related to one given context model for the binary arithmetic decoder and the binary symbol is from a plurality of binary symbols associated with the context model. Furthermore, the decoder may obtain a second probability for the binary symbol according to a second adaptation parameter, and then obtain a multi-hypothesis probability according to the first probability, a first adaptive weight, the second probability, and a second adaptive weight, where the multi-hypothesis probability determines a probability of the binary symbol equaling to a binary value. Moreover, the decoder may decode the binary symbol according to the multi-hypothesis probability.

A method including receiving a context-specific query, specific to the user, from a user device of a user. The method also includes creating a computer-readable data structure for storing data. The computer-readable data structure is specific to the context-specific query. The method also includes determining a feature related to the context-specific query. The method also includes retrieving a user value for the feature. The user value is retrieved from a data repository storing user-specific data that includes the user value. The method also includes modifying the computer-readable data structure to generate a modified computer-readable data structure by adding the feature and the user value for the feature to the computer-readable data structure. The method also includes applying a generative model to the modified computer-readable data structure to generate an output. The output includes a context-specific answer to the context-specific query. The method also includes returning the output.

Embodiments of the present disclosure provide a solution for video processing. A method for video processing is proposed. The method comprises: determining, for a conversion between a current video block of a video and a bitstream of the video, at least one context model associated with the current video block based on initiation information of the at least one context model, the initiation information of the at least one context model being included in the bitstream; and performing the conversion based on the at least one context model.

Herein provided are a feature encoding / decoding method, a recording medium storing a bitstream generated by the feature encoding method and a method of transmitting the bitstream. The feature decoding method may comprise obtaining channel information of a current coding unit, determining a context model for the current coding unit based on the channel information; and decoding the current coding unit based on the context model. The channel information may comprise channel grouping information of a feature map including the current coding unit, and the context model is determined based on the channel grouping information.

The invention provides an edge multi-mode grading detection elder falling early warning method comprising the following steps: deploying an edge computing network comprising a master control node and a data node, and loading a context model according to a position; binding the face information of the user with the RFID tag through an identity confirmation camera; the data node triggers a millimeter wave radar to collect point cloud data through a human body infrared sensor; performing contour change rate primary detection, context model deep learning secondary detection and voice module state confirmation tertiary response; and after the tumble is confirmed, the master control node gives an early warning to the emergency center. The invention relates to the technical field of elderly monitoring.

A system, computer program product, and a computer implemented method are provided for interfacing with a virtual dialog environment to dynamically and optimally collected context for problem diagnosis and resolution. A context model is leveraged to identify context entities, and one or more corresponding context collection mechanisms. The context model is implemented in real-time to facilitate dynamic selection of one or more of the identified context collection mechanisms, which are selectively subject to execution to resolve the problem diagnosis.

The invention discloses a short video promotion optimization method and system, relates to the technical field of short video promotion optimization, and aims to solve the problems that when an existing short video promotion method is used for processing subtle elements which may cause different understandings of users in video contents, intentions of merchants are difficult to accurately convey, target users are difficult to reach, and the user experience is poor. The method comprises the steps of obtaining content data of a to-be-promoted video, performing multi-meaning element identification on the content data to determine at least one potential multi-meaning element in the to-be-promoted video, and determining a situational misreading risk assessment result of the potential multi-meaning element according to the potential multi-meaning element and a preset user situation model, and according to the situational misreading risk assessment result, a pre-stored promotion constraint condition and a content modification cost model, making a decision between the content modification strategy and the audience screening strategy to generate an initial intervention strategy, executing the initial intervention strategy, and optimizing the promotion process based on user feedback data obtained after execution.

This disclosure describes a lightweight image query system that utilizes multiple lightweight models to quickly and efficiently generate responses to multimodal input queries. For example, in response to a multimodal input query, the lightweight image query system utilizes multiple lightweight context models to first obtain different types of context information based on the different input modes. The lightweight image query system then utilizes a lightweight large generative model (LGM) to quickly generate a query response using the different types of context information. By using lightweight models, including multiple lightweight context models and a lightweight LGM, the lightweight image query system can efficiently provide query responses to multimodal input queries in about half the time it takes conventional systems to return a query response.

The embodiment of the invention provides an end-to-end image compression method and system based on window local attention and generalized chessboard space channel context, and belongs to the technical field of image processing. The method comprises the following steps: constructing a transformation network based on an attention module and a stacked residual block; the transformation network based on the attention module and the stacked residual block is used for executing adaptive transformation of contents through dynamic representation and neighborhood information embedding to obtain potential features; establishing a generalized chessboard space channel context model; the generalized chessboard space channel context model is used for carrying out entropy coding on the potential features; and obtaining image compression data according to the transformation network based on the attention module and the stacked residual block and the generalized chessboard space channel context model. According to the method, redundancy can be eliminated to the maximum extent, excellent rate distortion performance is achieved, and meanwhile high-throughput parallel computing efficiency is ensured.

A method of video encoding includes performing context modeling to determine a context model for each of a number of bins of syntax elements corresponding to residues of a region of a transform skipped block in a current picture. The number of the bins of syntax elements being context coded does not exceed a maximum number of context coded bins set for the region. The method further includes encoding, according to Block Differential Pulse Code Modulation (BDPCM), the syntax elements based on the determined context models. When the maximum number of context coded bins is reached, remaining bins of syntax elements are encoded based on a bypass model.

The invention discloses a BIM-based construction resource real-time management method and system, and relates to the technical field of building construction. The method comprises the steps of obtaining real-time Internet of Things data flow and BIM model data of a construction site, dynamically associating physical resources in the Internet of Things data flow to a task component of a BIM model, and generating contextual consistency of fused sensing data and a current task; constructing a dynamic context model; inputting the current state of the dynamic context model into a preset construction knowledge graph, identifying an abnormal event, performing causal path tracing on the abnormal event, and outputting an abnormality diagnosis report at least including diagnosis confidence; and pushing the abnormality diagnosis report to a manager, receiving feedback data, and optimizing the inference rule of the construction knowledge graph by using the feedback data and the abnormality diagnosis report. According to the invention, the accuracy and adaptability of intelligent decision-making of construction resource management are effectively improved.

The invention discloses a design method for an on-orbit accompanying measurement and control communication machine state machine model, and the method comprises the steps: constructing a data model of a measurement and control communication machine state machine, and enabling the data model to be used for calling data externally; constructing a context model of the state machine of the measurement and control communication machine based on the data model, and constructing a logic architecture of the state machine of the measurement and control communication machine based on scene service data; based on the logic architecture, the context model obtains an executable extended state machine and updates the feature attributes; obtaining a simulation model through periodic continuation based on the state machine capable of executing expansion and the context model, wherein the simulation model is used for processing uplink and downlink remote control and telemetering, voice and distance measurement service data of satellite communication, responding to a communication machine instruction set of the state machine of the measurement and control communication machine and outputting communication machine product telemetering; universal configuration is implemented based on the communicator instruction set and the communicator product telemetry.

The present application provides a processing method, a processing device, and a storage medium. The processing method can be applied to a processing device and comprises: on the basis of information of a transform block, determining or obtaining a transform coefficient representation mode; and, on the basis of the transform coefficient representation mode and a context model, performing entropy encoding and / or entropy decoding. According to the technical solution of the present application, the accuracy of transform coefficient representation in transform coefficient entropy encoding and / or entropy decoding can be improved, thereby improving the efficiency of video encoding and / or decoding.

The invention discloses a power grid standard semantic understanding driven question answering system. The system comprises a user question receiving and preprocessing module, a question classification module, a real-time context integration module, a question processing and strategy selection module and an answer generation and user interaction module. The problem classification module classifies the problems based on a hybrid algorithm and determines the types of the problems; the real-time context integration module monitors and analyzes power grid data, weather conditions and maintenance records in real time, and constructs a current context model; the problem processing and strategy selection module is used for selecting the most suitable processing strategy according to the problem type and the real-time context; and the answer generation and user interaction module generates a natural language answer and displays the natural language answer to the user. By combining ChatGLM, CRF and prompt technologies, the system can efficiently and accurately process various power grid related problems and improve question and answer performance and user experience, is suitable for multiple fields such as power grid operation, maintenance and management, and has wide application prospects and remarkable practical value.

The invention relates to the technical field of automatic driving, in particular to an open type automatic driving safety management system and method supporting multi-benefit-party strategy arbitration, and the system comprises an open type safety strategy interface module which is used for defining a standardized strategy description specification; the strategy plug-in management module is used for receiving and loading the strategy plug-ins conforming to the strategy description specifications; the perception and fusion module is used for collecting the vehicle state data, the environment perception data and the service context data and fusing the data to generate a unified context model; the multi-strategy evaluation module is used for screening a candidate strategy set according to a unified context model and a trigger condition when an abnormal event is detected, and evaluating and selecting an optimal strategy; and the strategy execution and monitoring module is used for executing the action template corresponding to the optimal strategy. According to the invention, the automatic driving vehicle can select adaptive degradation strategies according to different operation scenes and business requirements, and the adaptability of the system to diversified business scenes is improved.

Various embodiments of the present disclosure relate generally to systems and methods for generating multi-view interactive digital media representations in a virtual reality environment. According to particular embodiments, a plurality of images is fused into a first content model and a first context model, both of which include multi-view interactive digital media representations of objects. Next, a virtual reality environment is generated using the first content model and the first context model. The virtual reality environment includes a first layer and a second layer. The user can navigate through and within the virtual reality environment to switch between multiple viewpoints of the content model via corresponding physical movements. The first layer includes the first content model and the second layer includes a second content model and wherein selection of the first layer provides access to the second layer with the second content model.

In various embodiments, a computer-implemented method for generating prompt inputs that describe design modifications includes receiving a first prompt input that indicates a first modification to a three-dimensional (3D) design, determining a first context input based on one or more attributes of the 3D design, and causing a first generative machine learning (ML) model to generate a second prompt input based on the first prompt input and the first context input, where the second prompt input indicates a second modification to the 3D design.

The embodiment of the application provides a kind of quantization parameter coding method and electronic equipment, the coding method includes: first, according to the QP reconstructed value of the QP of coded non-private CU in CU QP group, determine the QP prediction value of non-private CU to be coded in CU QP group;Then, according to the QP original value of non-private CU to be coded and the QP prediction value of non-private CU to be coded, determine the QP residual of non-private CU to be coded;Subsequently, according to the number of coded non-private CU in CU QP group, determine the context model corresponding to the QP residual of non-private CU to be coded;After that, according to the context model corresponding to the QP residual of non-private CU to be coded, the QP residual of non-private CU to be coded is entropy coded. In this way, it can be guaranteed that the decoding process of the QP of non-private CU at the decoding end and the encoding process of the QP of non-private CU at the encoding end are consistent.